[Colloquium] Finding Hidden Structure in Networks

There is more network data becoming available than humans can analyze by hand or eye. At the same time, much of this data is partial or noisy: nodes have attributes like demographics, location, and content that are partly known and partly hidden, many links are missing, and so on. How can we discover the important structures in a network, and use these structures to make good guesses about missing information? I will present a Bayesian approach based on generative models, powered by techniques from machine learning and statistical physics, with examples from food webs, word networks, and networks of documents. Along the way, we will think about what “structure” is anyway, and I will end with a cautionary note about how far we can expect to get when we think of “networks” in a purely topological way.

Bio:Cristopher Moore received his B.A. in Physics, Mathematics, and Integrated Science from Northwestern University, and his Ph.D. in Physics from Cornell. He has published over 100 papers at the boundary between physics and computer science, ranging from quantum computing, to phase transitions in NP-complete problems, to the theory of social networks and efficient algorithms for analyzing their structure. With Stephan Mertens, he is the author of The Nature of Computation, published by Oxford University Press. He is a Professor at the Santa Fe Institute.